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What is CRISP in Data Mining?

CRISP-DM stands for the cross-industry standard process for data mining. The CRISP-DM methodology provides a structured approach to planning a data mining project. It is a robust and well-proven methodology. We do not claim any ownership over it. We did not invent it. We are a converter of its powerful practicality, flexibility, and usefulness when using analytics to solve business issues. It is the golden thread that runs through almost every client meeting.

This model is an idealized sequence of events. In practice, many tasks can perform in a different order, and it will often be necessary to backtrack to previous tasks and repeat certain actions. The model does not try to capture all possible routes through the data mining process.

How does CRISP Help?

CRISP DM provides a roadmap, it gives you best practices, and it provides structures for better and faster results of using data mining, so that's how it helps the business follow while planning and carrying out a data mining project.

Phases of CRISP-DM

CRISP-DM provides an overview of the data mining life cycle as a process model. The life cycle model comprises six phases, with arrows indicating the most important and frequent dependencies between phases. The sequence of the phases is not strict. And most projects move back and forth between phases as necessary. The CRISP-DM model is flexible and can be customized easily.

For example, if your organization aims to detect money laundering, you will likely sift through large amounts of data without a specific modelling goal. Instead of modelling, your work will focus on data exploration and visualization to uncover suspicious patterns in financial data. CRISP-DM allows you to create a data mining model that fits your needs.

It includes descriptions of typical phases of a project, the tasks involved with each phase, and an explanation of the relationships between these tasks.

What is CRISP in Data Mining

Phase 1: Business Understanding

The first stage of the CRISP-DM process is understanding what you want to accomplish from a business perspective. Your organization may have competing objectives and constraints that must be properly balanced. This process stage aims to uncover important factors influencing the project's outcome. Neglecting this step can mean much effort is put into producing the right answers to the wrong questions.

What are the desired outputs of the project?

  1. Set objectives: Describe your primary objective from a business perspective. There may also be other related questions that you would like to mention. For example, your primary goal might be to keep current customers by predicting when they are prone to move to a competitor.
  2. Produce project plan: Describe the plan for achieving the data mining and business goals. The plan should specify the steps to perform during the rest of the project, including the initial selection of tools and techniques.
  3. Business success criteria: Here, you'll lay out the criteria you'll use to determine whether the project has been successful from the business point of view. These should ideally be specific and measurable, for example, reducing customer beat to a certain level. However, sometimes it might be necessary to have more subjective criteria, such as giving useful insights into the relationships.

Assess the current situation

This involves more detailed fact-finding about the resources, constraints, assumptions and other factors you'll need to consider when determining your data analysis goal and project plan.

  1. Inventory of resources: List the resources available to the project, including:
    • Personnel (business experts, data experts, technical support, data mining experts)
    • Data (fixed extracts, access to live, warehoused, or operational data)
    • Computing resources (hardware platforms)
    • Software (data mining tools, other relevant software)
  2. Requirements, assumptions and constraints: List all requirements of the project, including the schedule of completion, the required comprehensibility and quality of results, and any data security concerns and legal issues. Make sure that you are allowed to use the data. List the assumptions made by the project. These may be assumptions about the data that can be verified during data mining but may also include non-verifiable assumptions about the business related to the project. It is important to list the latter if they affect the validity of the results. List the constraints on the project. These may be constraints on the availability of resources but may also include technological constraints such as the size of the data set that it is practical to use for modelling.
  3. Risks and contingencies: List the risks or events that might delay the project or cause it to fail. List the corresponding contingency plans, like what action will you take if these risks or events occur?
  4. Terminology: Compile a glossary of terminology relevant to the project. This will generally have two components:
    • A glossary of relevant business terminology forms part of the business understanding available to the project. Constructing this glossary is a useful "knowledge elicitation" and education exercise.
    • A glossary of data mining terminology is illustrated with examples relevant to the business problem.
  5. Costs and benefits: Construct a cost-benefit analysis for the project, which compares the project's costs with the potential benefits to the business if it is successful. This comparison should be as specific as possible. For example, you should use financial measures in a commercial situation.

Determine data mining goals

A business goal states objectives in business terminology. A data mining goal states project objectives in technical terms. For example, the business goal might be Increase catalogue sales to existing customers. A data mining goal might be to Predict how many widgets a customer will buy, given their purchases over the past three years, demographic information (age, salary, city, etc.), and the item's price.

  1. Business success criteria: It describes the intended outputs of the project that enable the achievement of the business objectives.
  2. Data mining success criteria: It defines the criteria for a successful project outcome. For example, a certain level of predictive accuracy or a propensity-to-purchase profile with a given degree of "lift." As with business success criteria, it may be necessary to describe these in subjective terms, in which case the person or persons making the subjective judgment should be identified.

Produce project plan

Describe the intended plan for achieving the data mining goals and business goals. Your plan should specify the steps to perform during the rest of the project, including the initial selection of tools and techniques.

1. Project plan: List the stages to be executed in the project, with their duration, resources required, inputs, outputs, and dependencies. Where possible, try and make explicit the large-scale iterations in the data mining process, for example, repetitions of the modelling and evaluation phases.

As part of the project plan, it is important to analyze the dependencies between time schedules and risks. Mark the results of these analyses explicitly in the project plan, ideally with actions and recommendations if the risks are manifested. Decide which evaluation strategy will be used in the evaluation phase.

Your project plan will be a dynamic document. At the end of each phase, you'll review progress and achievements and update the project plan accordingly. Specific review points for these updates should be part of the project plan.

2. Initial assessment of tools and techniques: At the end of the first phase, you should undertake an initial assessment of tools and techniques. For example, you select a data mining tool that supports various methods for different stages of the process. It is important to assess tools and techniques early in the process since the selection of tools and techniques may influence the entire project.

Phase 2: Data Understanding

The second phase of the CRISP-DM process requires you to acquire the data listed in the project resources. This initial collection includes data loading if this is necessary for data understanding. For example, if you use a specific tool for data understanding, it makes perfect sense to load your data into this tool. If you acquire multiple data sources, you need to consider how and when you will integrate these.

  • Initial data collection report: List the data sources acquired, their locations, the methods used to acquire them, and any problems encountered. Record problems you encountered and any resolutions achieved. This will help with future replication of this project and the execution of similar future projects.

Describe data

Examine the "gross" or "surface" properties of the acquired data and report on the results.

  • Data description report: Describe the data that has been acquired, including its format, its quantity, the identities of the fields and any other surface features which have been discovered. Evaluate whether the data acquired satisfies your requirements.

Explore data

During this stage, you'll address data mining questions using querying, data visualization and reporting techniques. These may include:

  • Distribution of key attributes
  • Relationships between pairs or small numbers of attributes
  • Results of simple aggregations
  • Properties of significant sub-populations
  • Simple statistical analyses

These analyses may directly address your data mining goals. They may contribute to or refine the data description and quality reports and feed into the transformation and other data preparation steps needed for further analysis.

  • Data exploration report: Describe the results of your data exploration, including the first findings or initial hypothesis and their impact on the remainder of the project. If appropriate, you could include graphs and plots here to indicate data characteristics that suggest further examination of interesting data subsets.

Verify data quality

Examine the quality of the data, addressing questions such as:

  • Is the data complete, or does it cover all the cases required?
  • Is it correct, or does it contain errors, and if there are errors, how common are they?
  • Are there missing values in the data? If so, how are they represented, where do they occur, and how common are they?

Data quality report

List the results of the data quality verification. If quality problems exist, suggest possible solutions. Solutions to data quality problems generally depend heavily on data and business knowledge.

Phase 3: Data Preparation

In this project phase, you decide on the data you will use for analysis. The criteria you might use to make this decision include the relevance of the data to your data mining goals, the data's quality, and technical constraints such as limits on data volume or data types.

  • The rationale for inclusion/exclusion: List the data to be included/excluded and the reasons for these decisions.

Clean your data

This task involves raising the data quality to the level required by the analysis techniques that you've selected. This may involve selecting clean subsets of the data, the insertion of suitable defaults, or more ambitious techniques such as estimating missing data by modelling.

  • Data cleaning report: Describe what decisions and actions you took to address data quality problems. Consider any data transformations made for cleaning purposes and their possible impact on the analysis results.

Construct required data

This task includes constructive data preparation operations such as producing derived attributes, entire new records, or transformed values for existing attributes.

  • Derived attributes: These are new attributes constructed from one or more existing attributes in the same record. For example, you might use the variables of length and width to calculate a new variable of area.
  • Generated records: Here, you describe the creation of any completely new records. For example, you might need to create records for customers who did not purchase during the past year. There was no reason to have such records in the raw data. Still, it might make sense to represent that particular customers explicitly made zero purchases for modelling purposes.

Integrate data

These methods combine information from multiple databases, tables or records to create new records or values.

  • Merged data: Merging tables refers to joining two or more tables with different information about the same objects. For example, a retail chain might have one table with information about each store's general characteristics (e.g., floor space, type of mall), another table with summarized sales data (e.g., profit, percent change in sales from the previous year), and another with information about the demographics of the surrounding area. Each of these tables contains one record for each store. These tables can be merged into a new table with one record for each store, combining fields from the source tables.
  • Aggregations: Aggregations are operations in which new values are computed by summarizing information from multiple records or tables. For example, converting a table of customer purchases where one record for each purchase into a new table and one record for each customer, with fields such as the number of purchases, average purchase amount, percent of orders charged to credit card, percent of items under promotion etc.

Phase 4: Modelling

Select modelling technique: As the first step, you'll select the basic modelling technique you will use. Although you may have already selected a tool during the business understanding phase, at this stage, you'll be selecting the specific modelling technique, e.g. decision-tree building with C5.0 or neural network generation with back propagation. If multiple techniques are applied, perform this task separately for each technique.

  • Modelling technique: Document the basic modelling technique that is to be used.
  • Modelling assumptions: Many modelling techniques make specific assumptions about the data, for example, that all attributes have uniform distributions, no missing values are allowed, the class attribute must be symbolic etc. Record any assumptions made.

Generate test design

Before you build a model, you need to generate a procedure or mechanism to test the model's quality and validity. For example, in supervised data mining tasks such as classification, it is common to use error rates as quality measures for data mining models. Therefore, you typically separate the dataset into train and test sets, build the model on the train set, and estimate its quality on the separate test set.

  • Test design: Describe the intended plan for training, testing, and evaluating the models. A primary component of the plan is determining how to divide the available dataset into training, test and validation datasets.

Build model

Run the modelling tool on the prepared dataset to create one or more models.

  • Parameter settings: With any modelling tool, there are often a large number of parameters that can be adjusted. List the parameters, their values, and the rationale for selecting parameter settings.
  • Models: These are the models produced by the modelling tool, not a report on the models.
  • Model descriptions: Describe the resulting models, report on the interpretation of the models and document any difficulties encountered with their meanings.

Assess model

Interpret the models according to your domain knowledge, data mining success criteria, and desired test design. Judge the success of the application of modelling and discovery techniques, and then contact business analysts and domain experts later to discuss the data mining results in the business context. This task only considers models, whereas the evaluation phase also considers all other results produced during the project.

At this stage, you should rank the models and assess them according to the evaluation criteria. You should consider the business objectives and success criteria as far as you can here. In most data mining projects, a single technique is applied more than once, and data mining results are generated with several different techniques.

  • Model assessment: Summaries the results of this task, list the qualities of your generated models (, in terms of accuracy) and rank their quality with each other.
  • Revised parameter settings: According to the model assessment, revise them and tune them for the next modelling run. Iterate model building and assessment until you strongly believe that you have found the best model(s). Document all such revisions and assessments.

Phase 5: Evaluation

Evaluate your results: Previous evaluation steps dealt with factors such as the accuracy and generality of the model. During this step, you'll assess the degree to which the model meets your business objectives and seek to determine if there is some business reason why this model is deficient. Another option is to test the model on test applications in the real application if time and budget constraints permit. The evaluation phase also involves assessing any other data mining results you've generated. Data mining results involve models that are necessarily related to the original business objectives and all other findings that are not necessarily related to the original business objectives but might also unveil additional challenges, information, or hints for future directions.

  • Assessment of data mining results: Summarise assessment results in business success criteria, including a final statement regarding whether the project already meets the initial business objectives.
  • Approved models: After assessing models to business success criteria, the generated models that meet the selected criteria become the approved models.

Review process

At this point, the resulting models appear to be satisfactory and satisfy business needs. It is now appropriate for you to do a more thorough review of the data mining engagement to determine if there is an important factor or task that has somehow been overlooked. This review also covers quality assurance issues. For example: did we correctly build the model? Did we use only the attributes that we are allowed to use and that are available for future analyses?

  • Review of the process: Summarise the process review and highlight activities that have been missed and those that should be repeated.

Determine next steps

You now decide how to proceed depending on the assessment results and the process review. Do you finish this project and move on to deployment, initiate further iterations, or set up new data mining projects? You should also take stock of your remaining resources and budget, which may influence your decisions.

  • List of possible actions: List the potential further actions and the reasons for and against each option.
  • Decision: Describe the decision on how to proceed, along with the rationale.

Phase 6: Deployment

Plan deployment: In the deployment stage, you'll take your evaluation results and determine a strategy for their deployment. If a general procedure has been identified to create the relevant model(s), this procedure is documented here for later deployment. It makes sense to consider the ways and means of deployment during the business understanding phase because deployment is crucial to the project's success. This is where predictive analytics helps improve your business's operational side.

  • Deployment plan: Summarise your deployment strategy, including the necessary steps and how to perform them.

Plan monitoring and maintenance

Monitoring and maintenance are important issues if the data mining result becomes part of the day-to-day business and its environment. The careful preparation of a maintenance strategy helps to avoid unnecessarily long periods of incorrect usage of data mining results. The project needs a detailed monitoring process plan to monitor the deployment of the data mining result(s). This plan takes into account the specific type of deployment.

  • Monitoring and maintenance plan: Summarise the monitoring and maintenance strategy, including the necessary steps and how to perform them.

Produce final report

At the end of the project, you will write a final report. Depending on the deployment plan, this report may be only a summary of the project and its experiences (if they have not already been documented as an ongoing activity), or it may be a final and comprehensive presentation of the data mining result.

  • Final report: This is the final written report of the data mining engagement. It includes all of the previous deliverables, summarising and organizing the results.
  • Final presentation: There will often be a meeting after the project at which the results are presented to the customer.

Review project

Assess what went right and wrong, what was done well and what needs improvement.

  • Experience documentation: Summarise important experience gained during the project. For example, this documentation could include any pitfalls you encountered, misleading approaches, or hints for selecting the best-suited data mining techniques in similar situations. In ideal projects, experience documentation also covers any reports that individual project members have written during previous phases of the project.

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